2024/25 Undergraduate Programme Catalogue

BSc Data Science

Total credits: 360

Entry requirements:

Entry Requirements are available on the Course Search entry

School/Unit responsible for the parenting of students and programme:

School of Mathematics

Examination board through which the programme will be considered:

Relevant QAA Subject Benchmark Groups:

QAA SBS:
Mathematics, Statistics and Operational Research (March 2023)
Computing (March 2022)
QAA and Advance HE (2021) Education for Sustainable Development Guidance

Programme specification:

This course is, by design, multidisciplinary and follows a centred approach. In first year, the foundations are laid for mathematics, programming and communication, which are then further developed in second and third year.
The course delivery brings together scientific method, teamwork, communication, collaboration and creative problem solving. These skills will be blended in data science applications such as modelling, simulations, creating mock data sets and optimisation questions. You will use basic illustrative examples and motivational real-world case studies to understand how this foundational knowledge underpins advanced data science tools and techniques – and how they could be applied to help solve grand challenges too.
You will also have the chance to tailor your course with a selection of optional modules – from statistics, applied and pure mathematics to a range of exciting discovery modules – and get involved in your own 40-credit research project in third year.
The programme will aim to equip you to
• Be able to explain and apply data science core concepts, tools and techniques from mathematical modelling, machine learning, programming, software engineering and communication.
• Be analytical and able to critically evaluate different data science-related approaches, arguments and analyses, and to perform rigorous, robust and reproducible mathematical and computational analyses yourself.
• Be able to conduct independent research or projects - self-managing and appropriately drawing on different sources for information and support - synthesising and integrating the findings critically.
• Be able to appropriately apply fundamental principles of communication in context and to communicate advanced data science concepts, conclusions and recommendations to specialist and non-specialist audiences.
• Be able to express your ideas computationally and to apply principles of programming and software engineering to create, implement or use software appropriately in the analysis and visualisation of big data.
• Apply, blend and contribute your interdisciplinary skills and insights in multiprofessional and multidisciplinary teams, working collaboratively, creatively and professionally in diverse teams, on complex problems from industry to global challenges (e.g. sustainability).
• Be able to articulate how mathematics and data science relate to society and common practices, discuss ethical questions around data science, and act with professional integrity in accordance with ethical professional codes of practice as well as your own values.
• Be a strategic and reflective thinker, being able to identify, articulate and evidence past development and achievements, and plan future strategic goals, such as personal development, with reference to external frameworks.

Year1 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates will be required to study the following compulsory modules

Year2 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

We are currently refreshing our courses to make sure students have the best possible experience. Full module details for years 2 and 3 are not yet available. Before you enter years 2 and 3 details of modules for those years will be provided.

Compulsory modules:

Candidates will be required to study the following compulsory modules:
- Graphs, Networks and Systems - 20 credits
- Machine Learning and Object-oriented Programming - 20 credits
- Statistical Methods - 20 credits
- Stochastic Processes - 10 credits
- Time Series - 10 credits
- Further Linear Algebra and Discrete Mathematics - 20 credits

Optional modules:

Candidates will be required to study 0-20 credits from the following optional modules in Semester 1:
- Investigations in Mathematics - 10 credits
- Vector Calculus & Transforms - 20 credits

Discovery modules:

Depending on how many optional module credits are selected, candidates will then be required to study between 0 and 20 credits of Discovery modules in Semester 1.

Candidates must study a total of 120 credits in their second year. These should be distributed evenly across the semesters.

Year3 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

We are currently refreshing our courses to make sure students have the best possible experience. Full module details for year 3 are not yet available. Before you enter year 3 full details of modules for that year will be provided.

Compulsory modules:

Candidates will be required to study the following compulsory modules:
- Deep Learning and Explainable AI - 20 credits
- Data Curation and Governance - 20 credits
- Project in Data Science - 40 credits

Optional modules:

Candidates are required to select one of the following optional Semester 1 modules:
- Methods of Applied Mathematics - 20 credits
- Groups and Symmetry - 20 credits
- Statistical Modelling - 20 credits

Candidates are required to select one of the following optional Semester 2 modules:
- Multivariate Analysis and Classification - 20 credits
- Graph Theory and Combinatorics - 20 credits
- Mathematical Biology - 20 credits
- Numbers and Codes - 20 credits
- Entropy & Quantum Mechanics - 20 credits

Discovery modules:

Alternatively, candidates can replace one of the above 20 credit optional modules with a maximum of 20 credits of Discovery modules in that same semester.

Candidates must study a total of 120 credits in their third year. These should be distributed evenly across the semesters.

Last updated: 02/07/2024 12:00:33

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